Acquiescence Bias Inflates Estimates of Conspiratorial Beliefs and Political Misperceptions

Hill, Seth J.; Roberts, Margaret E.

doi:10.1017/pan.2022.28

Cited by 15 publications

(6 citation statements)

References 43 publications

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“…In the same vein, conspiratorial beliefs are notoriously difficult to measure and surveys tend to exaggerate their prevalence (Clifford et al, 2019). For instance, participants in survey experiments display a preference for positive response options (yes versus no, or agree versus disagree) which inflates agreement with statements, including conspiracy theories, by up to 50% (Hill & Roberts, 2021;Krosnick, 2018). Moreover, the absence of "Don't know" options, together with the impossibility to express one's preference for conventional explanations in comparison to conspiratorial explanations, greatly overestimate the prevalence of conspiratorial beliefs (Clifford et al, 2019).…”

Section: A Large Number Of People Are Misinformedmentioning

confidence: 99%

Misinformation on Misinformation: Conceptual and Methodological Challenges

Altay

Berriche

Acerbi

2023

Social Media + Society

190

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Alarmist narratives about online misinformation continue to gain traction despite evidence that its prevalence and impact are overstated. Drawing on research examining the use of big data in social science and reception studies, we identify six misconceptions about misinformation and highlight the conceptual and methodological challenges they raise. The first set of misconceptions concerns the prevalence and circulation of misinformation. First, scientists focus on social media because it is methodologically convenient, but misinformation is not just a social media problem. Second, the internet is not rife with misinformation or news, but with memes and entertaining content. Third, falsehoods do not spread faster than the truth; how we define (mis)information influences our results and their practical implications. The second set of misconceptions concerns the impact and the reception of misinformation. Fourth, people do not believe everything they see on the internet: the sheer volume of engagement should not be conflated with belief. Fifth, people are more likely to be uninformed than misinformed; surveys overestimate misperceptions and say little about the causal influence of misinformation. Sixth, the influence of misinformation on people’s behavior is overblown as misinformation often “preaches to the choir.” To appropriately understand and fight misinformation, future research needs to address these challenges.

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Section: A Large Number Of People Are Misinformedmentioning

confidence: 99%

Misinformation on Misinformation: Conceptual and Methodological Challenges

Altay

Berriche

Acerbi

2023

Social Media + Society

190

View full text Add to dashboard Cite

show abstract

“…In the acquiescence bias, survey respondents exhibit a tendency to favor positive response options or express a positive sentiment in a disproportionately frequent manner [ 55 ]. Previous work by [ 56 ] using China and the US as case studies, for example, showed that acquiescence bias can inflate estimated incidence of conspiratorial beliefs and political perceptions by as much as 50%. Related to this is the dissent bias where people tend to express a negative agreement in a more frequent manner [ 57 ].…”

Section: Related Workmentioning

confidence: 99%

Understanding the determinants of vaccine hesitancy in the United States: A comparison of social surveys and social media

Sasse,

Mahabir,

Gkountouna

et al. 2024

PLoS ONE

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The COVID-19 pandemic prompted governments worldwide to implement a range of containment measures, including mass gathering restrictions, social distancing, and school closures. Despite these efforts, vaccines continue to be the safest and most effective means of combating such viruses. Yet, vaccine hesitancy persists, posing a significant public health concern, particularly with the emergence of new COVID-19 variants. To effectively address this issue, timely data is crucial for understanding the various factors contributing to vaccine hesitancy. While previous research has largely relied on traditional surveys for this information, recent sources of data, such as social media, have gained attention. However, the potential of social media data as a reliable proxy for information on population hesitancy, especially when compared with survey data, remains underexplored. This paper aims to bridge this gap. Our approach uses social, demographic, and economic data to predict vaccine hesitancy levels in the ten most populous US metropolitan areas. We employ machine learning algorithms to compare a set of baseline models that contain only these variables with models that incorporate survey data and social media data separately. Our results show that XGBoost algorithm consistently outperforms Random Forest and Linear Regression, with marginal differences between Random Forest and XGBoost. This was especially the case with models that incorporate survey or social media data, thus highlighting the promise of the latter data as a complementary information source. Results also reveal variations in influential variables across the five hesitancy classes, such as age, ethnicity, occupation, and political inclination. Further, the application of models to different MSAs yields mixed results, emphasizing the uniqueness of communities and the need for complementary data approaches. In summary, this study underscores social media data’s potential for understanding vaccine hesitancy, emphasizes the importance of tailoring interventions to specific communities, and suggests the value of combining different data sources.

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“…Previous findings have pointed out the high demands on reputation, intense competition for funding and publications, and enormous challenges when leading diverse faculty, staff, and students are all impacting (Urbina-Garcia, 2020;Kruse et al, 2020). It is worth he existing EI instruments depend on self-report and suffer from social desirability or acquiescence bias (van de Mortel, 2005;Hill & Roberts, 2023), leading to subjective and untruthful results. The widely used scholarly databases have -driven way in this project.…”

Section: Introductionmentioning

confidence: 99%

Emotional Intelligence in Science

Yang,

Zhang,

Ding

2023

Proceedings of the Association for Information Science and Tech

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This is the first on‐going project presenting a four‐dimension metric to identify scholars' emotional intelligence (EI), which has yet to receive much attention. Current study proposes a data‐driven metric rather than a subjective survey to reflect EI's first dimension, self‐awareness. By employing paired‐T‐tests on the DBLP dataset, we found that highly self‐aware scholars are more likely to strive to improve with higher stability, leading to higher productivity and impact. Meanwhile, they have a more significant number of higher diverse collaborators. This research highlights the importance of one's self‐awareness to his/her scientific performance.

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Acquiescence Bias Inflates Estimates of Conspiratorial Beliefs and Political Misperceptions

Cited by 15 publications

References 43 publications

Misinformation on Misinformation: Conceptual and Methodological Challenges

Misinformation on Misinformation: Conceptual and Methodological Challenges

Understanding the determinants of vaccine hesitancy in the United States: A comparison of social surveys and social media

Emotional Intelligence in Science

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